Peter Zeidman is Chair of the Methods Group at the Wellcome Centre for Human Neuroimaging at University College London. After starting out in software engineering, he retrained in neuroscience, completing his PhD in 2014 with Professor Eleanor Maguire at UCL on the functional anatomy of the hippocampus. He then joined Professor Karl Friston’s team, developing the SPM software package for neuroimaging analysis, with a focus on Bayesian analysis methods. In his own research, Peter applies the analysis tools he develops to investigate the neurobiology of memory in healthy and disordered ageing.
Dynamic Causality Modeling
To investigate the relationship between structural and functional brain imaging measures, we need three key ingredients. First, we need hypotheses about the underlying biological mechanisms that give rise to both structural and functional data. Next, we need to translate these hypotheses into formal mathematical models. Finally, we need statistical machinery to compare the relative evidence for each model, given all the available multi-modal data. This modelling process is facilitated by the Dynamic Causal Modelling (DCM) framework, which is a well-established method and software suite that pairs differential equation models with Bayesian methods for testing hypotheses. In this talk I will provide an overview of DCM, with a focus on recent applications to multi-modal integration of brain structure and